Tomoe Hano

Papers

Total Citations

H-Index

About

Tomoe Hano’s research lies at the intersection of robotics, machine learning, and human-robot interaction, with a particular focus on enabling robots to learn from and respond to visual information. Her most cited work, “A Hand Image Instruction Learning System Using Transient-SOM” (2007, 8 citations), introduces a practical system for partner robots that uses a novel self-organizing map architecture to classify hand gestures and map them to appropriate actions. This four-layer system—comprising a feature map and an action map—demonstrates a pioneering approach to real-time, vision-based robot instruction. In her related work, “Robot Feeling Formation Based on Image Features” (2007, 5 citations), Hano explores how robots can develop internal feeling states by categorizing environmental visual features, a step toward more emotionally intelligent machines. Though her citation counts are modest, her contributions are notable for their early integration of unsupervised learning with robotic emotion and action systems. Hano’s research offers foundational insights for students and researchers interested in embodied cognition, affective computing, and adaptive human-robot collaboration.